Semi-Automating Curation of Clinical Practice Guidelines

The rapid expansion of clinical knowledge presents significant challenges for maintaining current and comprehensive clinical practice guidelines (CPGs). Manual curation processes are resource-intensive and often result in delayed integration of new evidence. We present a reproducible pipeline for semi-automated surveillance of clinical practice guidelines (CPGs). The system ingests heterogeneous guideline documents, parses main text (PyMuPDF) and segments sentences (spaCy), normalizes terms via a concept layer, embeds passages with biomedical language models, and indexes them with FAISS for dense retrieval. A retrieval-augmented generation (RAG) step drafts editorial suggestions with provenance for expert review. PubMedBERT achieved highest performance across metrics, with 80% inter-annotator agreement, supporting expert-guided AI guideline updates.